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Commun Biol. 2019 May 14;2:183. doi: 10.1038/s42003-019-0415-5. eCollection 2019.

diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering.

Author information

1Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland.
2SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland.
3Present Address: F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland.
4Present Address: Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, CH-4058 Basel, Switzerland.


High-dimensional flow and mass cytometry allow cell types and states to be characterized in great detail by measuring expression levels of more than 40 targeted protein markers per cell at the single-cell level. However, data analysis can be difficult, due to the large size and dimensionality of datasets as well as limitations of existing computational methods. Here, we present diffcyt, a new computational framework for differential discovery analyses in high-dimensional cytometry data, based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Our approach provides improved statistical performance, including for rare cell populations, along with flexible experimental designs and fast runtimes in an open-source framework.


Cell signalling; Software; Statistical methods

Conflict of interest statement

The authors declare no competing interests.

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